10 papers · ranked by Valyu relevance
Bian Bian, Yiming Zhang, Hongmin Li, Jiuzhou Zhong + 1 more
mRNA design plays a central role in synthetic biology, nucleic acid therapeutics, and vaccine development. Although large language models are applied in many biological fields, generative language models for de novo mRNA design remains largely unexplored. Here, we introduce mRNA-GPT, a series of generative mRNA…
Bian Bian, Yiming Zhang, Jichen Zhang, Kiyoshi Asai + 1 more
mRNA coding sequence design is a critical component in the development of mRNA vaccines, nucleic acid therapeutics, and heterologous gene expression systems. While large language models have recently been successfully applied to protein design and RNA modeling, designing optimal mRNA coding sequences for a given…
Mei Lang, Xingyu Fang, Zhen Wang, Mingxuan Chen + 5 more
Although mRNA codon language models provide a generalizable framework for biological sequence design, effective CDS design requires both a learned sequence design space that captures biological constraints and context-configurable design preferences. Here we present CodonMamba, a codon language model framework for mRNA…
Shiyi Du, Gün Kaynar, Jiayi Li, Zhaoyi You + 2 more
Optimizing synonymous codon sequences to improve translation efficiency, RNA stability, and compositional properties is challenging because the search space grows exponentially with protein length and objectives interact through long range RNA structure. Dynamic programming-based methods can provide strong solutions…
Chien-Chun Chen, Yu-Chun Huang, Antonio Ortega, Raquel Suárez-Grimalt + 5 more
The roles of sleep in priming the brain for associative learning remain unclear. Here, we report that acute sleep deprivation in Drosophila selectively impairs pattern separation—the ability to distinguish between similar stimuli—without affecting classical conditioning. This deficit correlates with disrupted sparse…
Witold E. Wolski, Leonardo Schwarz, Christian Trachsel, Martina Zanella + 6 more
Mass spectrometry laboratories must turn lists of submitted samples into acquisition queues. The run order and the placement of quality-control (QC) injections determine whether a design controls batch effects and signal drift, and whether those effects stay correctable afterward. Yet operators usually set them by hand…
Priya Chakraborty, Subrata Dey, Ranu Kundu, Malay Banerjee + 1 more
Exploring the emergence of spatio-temporal patterns due to nonlinearities in gene expression is a relatively new development. In this work, we explore the effect of resource constraint on gene regulatory motif from both equilibrium and spatio-temporal standpoint, taking into consideration the degradation class of…
Thomas A. Hopf, Artem Gazizov, Sergio Garcia Busto, Ethan Eschbach + 9 more
Machine learning methods for protein engineering are rarely interoperable, require bespoke workflows, and remain inaccessible to non-experts. Yet the design problems that matter most – conditional design subject to real-world constraints, multi-objective optimization, and iterative lab-in-the-loop workflows where…
Cyrus M. Haas, Sanela Rankovic, Hanul K. Lewis, Kenneth D. Carr + 22 more
Computational design of self-assembling proteins has long relied on pre-existing structures and sequences, fundamentally limiting control over their structural and functional properties. Recent machine learning-based methods have transformed our ability to design functional small de novo proteins and oligomers, yet…
Minchao Fang, Chentong Wang, Jungang Shi, Fengbai Lian + 10 more
Deep learning has revolutionized biomolecular modeling, enabling the prediction of diverse structures with atomic accuracy. However, leveraging the atomic-level precision of the structure prediction model for de novo design remains challenging. Here, we present HalluDesign, a general all-atom framework for protein…